Abstract

Prediction of interannual variability (IAV) of Indian summer monsoon (ISM) rainfall is limited by "internal" dynamics, and the monsoon intraseasonal oscillations (MISOs) seems to be at the heart of producing internal IAV of the ISM. If one could find an identifiable way through which these MISOs are modulated by slowly varying "external" forcing, such as El Nino-Southern Oscillation (ENSO), the uncertainty in the prediction of IAV could be reduced, leading to improvement of seasonal prediction. Such efforts, so far, have been inconclusive. In this study, the modulation of MISOs by ENSO is assessed by using a nonlinear pattern recognition technique known as the Self-Organizing Map (SOM). The SOM technique is efficient in handling the nonlinearity/event-to-event variability of the MISOs and capable of identifying various shades of MISO from large-scale dynamical/thermodynamical indices, without providing information on rainfall. It is shown that particular MISO phases are preferred during ENSO years, that is, the canonical break phase is preferred more in the El Nino years and the typical active phase is preferred during La Nina years. Interestingly, if the SOM clustering is done by removing the ENSO effect on seasonal mean, the preference for the break node remains relatively unchanged; whereas, the preference reduces/vanishes for the active node. The results indicate that the El Nino-break relationship is almost independent of the ENSO-monsoon relationship on seasonal scale whereas the La Nina-active association seems to be interwoven with the seasonal relationship.